code: def get_all_random_sampling_operators():
      """Gets all Random Sampling operators registered with MXNet.
  
      Returns
      -------
      {"operator_name": {"has_backward", "nd_op_handle", "params"}}
      """
      # Additional Random Sampling ops which do not start with "random_" or "sample_"
      additional_random_sampling_ops = {
          "GridGenerator",
          "BilinearSampler",
      }
  
      # Get all mxnet operators
      mx_operators = _get_all_mxnet_operators()
  
      # Filter for Random Sampling operators
      random_sampling_mx_operators = {}
      for op_name, _ in mx_operators.items():
          if (
              op_name.startswith(("random_", "sample_"))
              or op_name in additional_random_sampling_ops
          ):
              random_sampling_mx_operators[op_name] = mx_operators[
                  op_name
              ]
      return random_sampling_mx_operators
  
imports: from operator import itemgetter
  from mxnet import runtime
  import mxnet
  from benchmark.opperf.rules.default_params import DEFAULTS_INPUTS, DEFAULTS_INPUTS_LARGE_TENSOR, MX_OP_MODULE
label_desc: Use image node_id 10 for calling a global function with example usage: get_all_random_sampling_operators() and returns: random_sampling_mx_operators